Graph Mining
70 papers with code • 0 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Graph Mining
Libraries
Use these libraries to find Graph Mining models and implementationsDatasets
Yelp
Yelp-Fraud
Amazon-Fraud
Netzschleuder
IMCPT-SparseGM-100
Latest papers with no code
ChebMixer: Efficient Graph Representation Learning with MLP Mixer
In this paper, we present a novel architecture named ChebMixer, a newly graph MLP Mixer that uses fast Chebyshev polynomials-based spectral filtering to extract a sequence of tokens.
MuseGraph: Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining
Traditional Graph Neural Networks (GNNs), which are commonly used for modeling attributed graphs, need to be re-trained every time when applied to different graph tasks and datasets.
OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining
We envisage that OAG-Bench can serve as a common ground for the community to evaluate and compare algorithms in academic graph mining, thereby accelerating algorithm development and advancement in this field.
Learning Attributed Graphlets: Predictive Graph Mining by Graphlets with Trainable Attribute
LAGRA learns importance weights for small attributed subgraphs, called attributed graphlets (AGs), while simultaneously optimizing their attribute vectors.
Introducing New Node Prediction in Graph Mining: Predicting All Links from Isolated Nodes with Graph Neural Networks
This paper introduces a new problem in the field of graph mining and social network analysis called new node prediction.
Deep Efficient Private Neighbor Generation for Subgraph Federated Learning
Behemoth graphs are often fragmented and separately stored by multiple data owners as distributed subgraphs in many realistic applications.
Cost Aware Untargeted Poisoning Attack against Graph Neural Networks,
Graph Neural Networks (GNNs) have become widely used in the field of graph mining.
Self-supervised Heterogeneous Graph Variational Autoencoders
Instead of directly reconstructing raw features for attributed nodes, SHAVA generates the initial low-dimensional representation matrix for all the nodes, based on which raw features of attributed nodes are further reconstructed to leverage accurate attributes.
KG-MDL: Mining Graph Patterns in Knowledge Graphs with the MDL Principle
Second, real-life KGs tend to differ from the graphs usually treated in graph mining: they are multigraphs, their vertex degrees tend to follow a power-law, and the way in which they model knowledge can produce spurious patterns.
Jaccard-constrained dense subgraph discovery
Finding dense subgraphs is a core problem in graph mining with many applications in diverse domains.
